System

Abstract

An object of a system according to an embodiment is to propose an optimum gift in consideration of the preference, budget, and gift giving timing of a receiver.SOLUTION: A system according to an embodiment includes an idea proposing part, a personalized gift proposing part, a cost managing part, a schedule managing part, a burden reducing part, and a regional industry activating part. The idea proposal unit proposes an optimum gift idea based on the latest trend and the past history in accordance with the type, preference, and scene of the receiver. The personalized gift proposing part proposes a gift customized or personalized to the individuality of the receiver. The cost management unit proposes an optimal gift within a range according to the budget of the user. The schedule management part notifies the user of the gift timing which the user tends to forget. The load reduction unit supports from a gift idea to delivery. The local industry vitalization unit provides information on local specialties and local industries.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made gift selection cumbersome, making it difficult to select the optimal gift that takes into account the recipient's preferences, budget, and timing of the gift.

[0005] The system according to the embodiment aims to propose the most suitable gift taking into consideration the recipient's preferences, budget, and timing of gift-giving. [Means for solving the problem]

[0006] The system according to the embodiment comprises an idea suggestion unit, a personalized gift suggestion unit, a cost management unit, a schedule management unit, a burden reduction unit, and a local industry revitalization unit. The idea suggestion unit suggests optimal gift ideas based on the latest trends and past history, tailored to the recipient's type, preferences, and occasion. The personalized gift suggestion unit suggests customized and personalized gifts that match the recipient's individuality. The cost management unit suggests optimal gifts within the user's budget. The schedule management unit notifies users of the timing of gift-giving, which is often forgotten. The burden reduction unit supports users from gift idea creation to delivery. The local industry revitalization unit provides information on regional specialties and local industries. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable gift taking into consideration the recipient's preferences, budget, and timing of gift-giving. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The gift communication system according to an embodiment of the present invention is a system that uses AI to make everyday "thank yous" a reality and makes buyers, recipients, and creators happy. This allows users to efficiently and effectively select and give gifts.

[0029] The gift communication system according to the embodiment includes an idea suggestion unit, a personalized gift suggestion unit, a cost management unit, a schedule management unit, a burden reduction unit, and a local industry revitalization unit. The idea suggestion unit suggests optimal gift ideas based on the latest trends and past history, tailored to the recipient's type, preferences, and occasion. For example, the idea suggestion unit suggests gifts based on the recipient's hobbies and interests. Furthermore, when a user asks, "I don't know what to give my mother for Mother's Day," the AI ​​generation unit suggests "gifts that match my mother's favorite flowers and hobbies." The personalized gift suggestion unit suggests customized and personalized gifts tailored to the recipient's personality. For example, the personalized gift suggestion unit analyzes the recipient's past history and preferences to select the optimal gift. The personalized gift suggestion unit can also suggest travel, activity experiences, meal vouchers, and the like. The cost management unit suggests optimal gifts within the user's budget. For example, the cost management unit receives a prompt such as, "I'm looking for a gift that matches my hobbies within a budget of 5,000 yen," and suggests optimal gifts within that budget. The schedule management unit notifies users of gift-giving timing, which is often forgotten. For example, the schedule management unit sends a reminder such as, "It's Mom's birthday next week. Have you prepared a gift?" The burden reduction unit supports users from gift ideas to delivery. For example, the burden reduction unit reduces stress and burden on users by "handling everything from gift selection to purchase and delivery arrangements all in one place." The local industry revitalization unit provides information on local specialties and local industries. For example, the local industry revitalization unit suggests "roadside station products" and "hometown tax donation products" to revitalize local industries. This allows users to efficiently and effectively select and give gifts. For example, when giving mothers' favorite flowers for Mother's Day, the generation AI suggests the most suitable flowers, purchases them within budget, and even arranges for delivery. Furthermore, giving local specialties contributes to the development of the local economy.

[0030] The idea suggestion unit can analyze a user's past gift selection history, learn selection trends, and reflect them in future suggestions. For example, the idea suggestion unit uses a generation AI to analyze a user's past gift selection history and learn selection trends. For example, the type and price range of gifts given in the past, as well as the recipient's reactions, are stored in a database and reflected in future suggestions. The idea suggestion unit also uses the generation AI to learn selection trends based on the user's past gift selection history and utilize this in future suggestions. For example, if there is a bias toward a particular brand or category, the unit makes suggestions taking this tendency into consideration. The idea suggestion unit also uses the generation AI to analyze a user's past gift selection history and learn selection trends, which are reflected in future suggestions. For example, the unit makes more appropriate suggestions based on ratings and reviews of gifts given in the past. This enables more accurate suggestions to be made based on the user's past selection history.

[0031] The idea suggestion unit can analyze public information on the recipient's social media, blogs, etc., and suggest gifts that reflect their latest interests and concerns. For example, the generation AI of the idea suggestion unit analyzes public information on the recipient's social media, blogs, etc., and suggests gifts that reflect their latest interests and concerns. For example, suggestions are made based on information about hobbies and interests recently posted by the recipient. The idea suggestion unit also analyzes the content of posts on the recipient's social media or blogs, and the generation AI grasps their latest interests and concerns. For example, gifts related to places the recipient has recently visited or events they have attended. The idea suggestion unit also analyzes public information on the recipient, and suggests gifts that reflect their latest interests and concerns. For example, suggestions are made based on products the recipient has recently purchased or reviewed. This makes it possible to make suggestions based on the recipient's latest interests and concerns.

[0032] The personalized gift suggestion unit analyzes the recipient's past purchase history and reviews to suggest more accurate personalized gifts. For example, the personalized gift suggestion unit uses a generation AI to analyze the recipient's past purchase history and reviews to suggest more accurate personalized gifts. For example, it suggests gifts similar to products that the recipient has given high ratings to in the past. The personalized gift suggestion unit also uses a generation AI to suggest personalized gifts based on the recipient's past purchase history and reviews. For example, it suggests products from brands and categories that the recipient frequently purchases. The personalized gift suggestion unit also uses a generation AI to analyze the recipient's purchase history and reviews to suggest personalized gifts. For example, it selects the most appropriate gift based on the reviews of products the recipient has purchased in the past. This enables more accurate suggestions to be made based on the recipient's past purchase history and reviews.

[0033] The personalized gift suggestion unit can collect the opinions of the recipient's family and friends and suggest the optimal gift from multiple perspectives. In the personalized gift suggestion unit, for example, the generation AI collects the opinions of the recipient's family and friends and suggests the optimal gift from multiple perspectives. For example, it suggests gifts that family and friends would like to give to the recipient. In addition, the personalized gift suggestion unit uses the generation AI to suggest the optimal gift based on the opinions of the recipient's family and friends. For example, if family and friends know the recipient's hobbies and preferences, it uses that information to make suggestions. In addition, the personalized gift suggestion unit uses the generation AI to collect the opinions of family and friends and suggest the optimal gift from multiple perspectives. For example, it collects gifts that family and friends would like to give to the recipient in the form of a questionnaire and makes suggestions based on the results. This makes it possible to make optimal suggestions from multiple perspectives based on the opinions of the recipient's family and friends.

[0034] The cost management unit can analyze a user's past spending history and suggest the optimal gift within a budget. In the cost management unit, for example, the generation AI analyzes a user's past spending history and suggests the optimal gift within a budget. For example, suggestions are made based on data on the amount of money the user has spent in the past and the products they have purchased. In addition, the cost management unit can use the generation AI to suggest the optimal gift within a budget based on the user's past spending history. For example, suggestions are made taking into account the price range and frequency of gifts the user has purchased in the past. In addition, the cost management unit can use the generation AI to analyze a user's spending history and suggest the optimal gift within a budget. For example, the generation AI can select the optimal gift within a budget based on ratings and reviews of gifts the user has purchased in the past. This makes it possible to make optimal suggestions within a budget based on the user's past spending history.

[0035] The cost management department can monitor market price fluctuations in real time and suggest the optimal timing for purchases. For example, the generation AI in the cost management department monitors market price fluctuations in real time and suggests the optimal timing for purchases. For example, it predicts when a particular product will go on sale and makes a suggestion. The cost management department also uses the generation AI to suggest the optimal timing for purchases based on market price fluctuations. For example, it predicts when prices will fall and suggests purchasing at that time. The cost management department also uses the generation AI to monitor market price fluctuations in real time and suggests the optimal timing for purchases. For example, it suggests purchasing during a particular event or sale period. This makes it possible to suggest the optimal timing for purchases based on market price fluctuations.

[0036] The schedule management unit can analyze the user's calendar and schedule and suggest the optimal timing for gift-giving. In the schedule management unit, for example, the generation AI analyzes the user's calendar and schedule and suggests the optimal timing for gift-giving. For example, it suggests a timing for gift-giving that matches the user's plans. In addition, the schedule management unit can have the generation AI analyze the user's calendar and schedule and suggest the optimal timing for gift-giving. For example, it can make suggestions to match specific events or anniversaries. In addition, the schedule management unit can have the generation AI analyze the user's calendar and schedule and suggest the optimal timing for gift-giving. For example, it can suggest a timing for gift-giving that avoids the user's busy periods. This makes it possible to suggest the optimal timing for gift-giving based on the user's calendar and schedule.

[0037] The schedule management unit can learn past gift-giving history and predict the timing of the next gift-giving. In the schedule management unit, for example, the generation AI learns past gift-giving history and predicts the timing of the next gift-giving. For example, it makes suggestions based on the timing of gifts given by the user in the past. In addition, the schedule management unit predicts the timing of the next gift-giving based on the past gift-giving history. For example, it makes suggestions to coincide with specific events or anniversaries. In addition, the schedule management unit predicts the timing of the next gift-giving based on the past gift-giving history. For example, it makes suggestions taking into account the frequency and timing of gifts given by the user in the past. This makes it possible to predict the timing of the next gift-giving based on the past gift-giving history.

[0038] The burden reduction unit can analyze the user's past gift selection process and propose the most efficient selection method. In the burden reduction unit, for example, the generation AI analyzes the user's past gift selection process and proposes the most efficient selection method. For example, the proposal is made based on the selection process of gifts selected by the user in the past. In addition, the burden reduction unit proposes the most efficient selection method based on the user's past gift selection process. For example, the proposal is made taking into account the selection time and steps of gifts selected by the user in the past. In addition, the burden reduction unit proposes the most efficient selection method based on the user's past gift selection process. For example, the proposal is made based on the ratings and reviews of gifts selected by the user in the past. This makes it possible to propose an efficient selection method based on the user's past selection process.

[0039] The Regional Industry Revitalization Department can work directly with producers of regional specialty products and provide the latest information. For example, the generation AI in the Regional Industry Revitalization Department works directly with producers of regional specialty products and provides the latest information. For example, it provides users with the latest arrival information from producers and the characteristics of specialty products. The Regional Industry Revitalization Department also works with producers of regional specialty products and the generation AI provides the latest information. For example, it provides users with information from producers about recommended products and seasonal limited items. The Regional Industry Revitalization Department also works directly with producers of regional specialty products and provides the latest information. For example, it provides users with information from producers about the manufacturing process and background of specialty products. In this way, by working directly with producers of regional specialty products and providing the latest information, it is possible to revitalize local industries.

[0040] The Regional Industry Revitalization Department can analyze local event information and suggest related specialty products. For example, the generation AI in the Regional Industry Revitalization Department analyzes local event information and suggests related specialty products. For example, it suggests specialty products that match local festivals and events. The Regional Industry Revitalization Department also uses the generation AI to suggest related specialty products based on local event information. For example, it suggests specialty products that match specific events or seasons. The Regional Industry Revitalization Department also uses the generation AI to analyze local event information and suggest related specialty products. For example, it suggests specialty products that are popular at local events or limited edition products. This makes it possible to suggest specialty products based on local event information.

[0041] The Regional Industry Revitalization Department can attract users' interest by providing stories and background information about regional specialty products. For example, the generation AI can provide stories and background information about regional specialty products to attract users' interest. For example, it can introduce the manufacturing process of the specialty product or anecdotes about the producer. The Regional Industry Revitalization Department also uses the generation AI to make suggestions that will attract users' interest based on the stories and background information about the regional specialty product. For example, it can introduce the history and traditions of the specialty product. The Regional Industry Revitalization Department can also use the generation AI to provide stories and background information about regional specialty products to attract users' interest. For example, it can introduce the manufacturing method of the specialty product and the characteristics of the materials used. In this way, it is possible to attract users' interest by providing stories and background information about the regional specialty product.

[0042] The Regional Industry Revitalization Department can propose tasting events and experience tours for regional specialty products. For example, the generation AI in the Regional Industry Revitalization Department proposes tasting events and experience tours for regional specialty products. For example, it proposes tasting events for specialty products and events to interact with producers. The Regional Industry Revitalization Department also makes suggestions to users based on tasting events and experience tours for regional specialty products. For example, it proposes tours that allow visitors to see the manufacturing site of specialty products. The generation AI in the Regional Industry Revitalization Department also proposes tasting events and experience tours for regional specialty products. For example, it proposes harvesting experiences for specialty products and cooking classes. This makes it possible to propose tasting events and experience tours for regional specialty products.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The gift communication system may further include an environmental consideration unit. The environmental consideration unit makes suggestions for minimizing the impact on the environment when selecting a gift. For example, the environmental consideration unit may suggest gifts made from eco-friendly materials or reusable packaging. The environmental consideration unit may also prioritize locally produced products or gifts with short delivery distances in order to reduce the carbon footprint. Furthermore, the environmental consideration unit may provide the user with a message encouraging environmental consideration when selecting a gift. This allows the user to make environmentally friendly choices and contribute to the realization of a sustainable society.

[0045] The gift communication system may further include a health management unit. The health management unit suggests health-conscious gifts based on the user's health condition and lifestyle. For example, the health management unit may suggest appropriate food gifts taking into account the user's dietary restrictions and allergy information. The health management unit may also suggest sports equipment and fitness-related gifts based on the user's exercise habits and fitness goals. Furthermore, the health management unit may monitor the user's stress level and sleep patterns and suggest gifts that are useful for relaxation and stress relief. This allows the user to select appropriate gifts while maintaining their health.

[0046] The gift communication system may further include an education support unit. The education support unit suggests education-related gifts based on the user's learning needs and interests. For example, the education support unit may suggest books and online courses related to the field the user wants to study. The education support unit may also suggest appropriate educational toys and teaching materials based on the user's child's learning progress and interests. Furthermore, the education support unit may provide information on workshops and seminars that the user can participate in to expand learning opportunities. This allows the user to select gifts that meet their own and their family's learning needs.

[0047] The gift communication system may further include a cultural exchange section. The cultural exchange section may suggest gifts that promote intercultural understanding and international exchange. For example, the cultural exchange section may suggest traditional crafts or specialty products from countries or regions in which the user is interested. The cultural exchange section may also provide information on events and tours that allow the user to experience different cultures. Furthermore, the cultural exchange section may suggest books or documentaries that will help the user deepen their knowledge of different cultures. This allows the user to enjoy international exchange through gifts while deepening their understanding of different cultures.

[0048] The gift communication system can also analyze a user's past gift selection history without using emotion estimation functions, learn selection trends, and reflect them in future suggestions. For example, the types and price ranges of gifts given in the past, as well as the recipients' reactions, can be saved in a database and reflected in future suggestions. Selection trends can also be learned based on the user's past gift selection history and used in future suggestions. For example, if there is a bias toward a particular brand or category, suggestions can be made taking these trends into account. Furthermore, more appropriate suggestions can be made based on ratings and reviews of gifts given in the past. This enables more accurate suggestions based on the user's past selection history.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The idea suggestion unit suggests optimal gift ideas based on the latest trends and past history, taking into account the recipient's type, preferences, and occasion. For example, if a user asks for advice such as "I don't know what to give for Mother's Day," the AI ​​generator will suggest "gifts that match Mom's favorite flowers and hobbies." Step 2: The personalized gift suggestion department proposes customized and personalized gifts tailored to the recipient's personality. For example, it can analyze the recipient's past purchase history and preferences to select the most suitable gift, and can also suggest travel, activity experiences, meal vouchers, etc. Step 3: The cost management department suggests the best gift within the user's budget. For example, it receives a prompt such as "I'm looking for a gift that matches my hobbies and has a budget of 5,000 yen or less," and suggests the best gift within that range. Step 4: The schedule management section will notify you of gift-giving timings that you may forget. For example, it will send a reminder such as, "It's Mom's birthday next week. Have you prepared a gift?" Step 5: The burden reduction department supports users from gift ideas to delivery. For example, by "handling everything from gift selection to purchase and delivery arrangements all in one place," the department reduces the stress and burden on users. Step 6: The Regional Industry Revitalization Department will provide information on regional specialties and local industries. For example, they will propose "roadside station products" and "hometown tax donation products" to revitalize regional industries.

[0051] (Example 2) The gift communication system according to an embodiment of the present invention is a system that uses AI to make everyday "thank yous" a reality and makes buyers, recipients, and creators happy. This allows users to efficiently and effectively select and give gifts.

[0052] The gift communication system according to the embodiment includes an idea suggestion unit, a personalized gift suggestion unit, a cost management unit, a schedule management unit, a burden reduction unit, and a local industry revitalization unit. The idea suggestion unit suggests optimal gift ideas based on the latest trends and past history, tailored to the recipient's type, preferences, and occasion. For example, the idea suggestion unit suggests gifts based on the recipient's hobbies and interests. Furthermore, when a user asks, "I don't know what to give my mother for Mother's Day," the AI ​​generation unit suggests "gifts that match my mother's favorite flowers and hobbies." The personalized gift suggestion unit suggests customized and personalized gifts tailored to the recipient's personality. For example, the personalized gift suggestion unit analyzes the recipient's past history and preferences to select the optimal gift. The personalized gift suggestion unit can also suggest travel, activity experiences, meal vouchers, and the like. The cost management unit suggests optimal gifts within the user's budget. For example, the cost management unit receives a prompt such as, "I'm looking for a gift that matches my hobbies within a budget of 5,000 yen," and suggests optimal gifts within that budget. The schedule management unit notifies users of gift-giving timing, which is often forgotten. For example, the schedule management unit sends a reminder such as, "It's Mom's birthday next week. Have you prepared a gift?" The burden reduction unit supports users from gift ideas to delivery. For example, the burden reduction unit reduces stress and burden on users by "handling everything from gift selection to purchase and delivery arrangements all in one place." The local industry revitalization unit provides information on local specialties and local industries. For example, the local industry revitalization unit suggests "roadside station products" and "hometown tax donation products" to revitalize local industries. This allows users to efficiently and effectively select and give gifts. For example, when giving mothers' favorite flowers for Mother's Day, the generation AI suggests the most suitable flowers, purchases them within budget, and even arranges for delivery. Furthermore, giving local specialties contributes to the development of the local economy.

[0053] The idea suggestion unit can analyze a user's past gift selection history, learn selection trends, and reflect them in future suggestions. For example, the idea suggestion unit uses a generation AI to analyze a user's past gift selection history and learn selection trends. For example, the type and price range of gifts given in the past, as well as the recipient's reactions, are stored in a database and reflected in future suggestions. The idea suggestion unit also uses the generation AI to learn selection trends based on the user's past gift selection history and utilize this in future suggestions. For example, if there is a bias toward a particular brand or category, the unit makes suggestions taking this tendency into consideration. The idea suggestion unit also uses the generation AI to analyze a user's past gift selection history and learn selection trends, which are reflected in future suggestions. For example, the unit makes more appropriate suggestions based on ratings and reviews of gifts given in the past. This enables more accurate suggestions to be made based on the user's past selection history.

[0054] The idea suggestion unit can analyze public information on the recipient's social media, blogs, etc., and suggest gifts that reflect their latest interests and concerns. For example, the generation AI of the idea suggestion unit analyzes public information on the recipient's social media, blogs, etc., and suggests gifts that reflect their latest interests and concerns. For example, suggestions are made based on information about hobbies and interests recently posted by the recipient. The idea suggestion unit also analyzes the content of posts on the recipient's social media or blogs, and the generation AI grasps their latest interests and concerns. For example, gifts related to places the recipient has recently visited or events they have attended. The idea suggestion unit also analyzes public information on the recipient, and suggests gifts that reflect their latest interests and concerns. For example, suggestions are made based on products the recipient has recently purchased or reviewed. This makes it possible to make suggestions based on the recipient's latest interests and concerns.

[0055] The idea suggestion unit can use the emotion estimation function to estimate the recipient's emotions and suggest a gift that best suits those emotions. For example, the idea suggestion unit uses the emotion estimation function to estimate the recipient's emotions and suggest a gift that best suits those emotions. For example, it selects a gift that will make the recipient feel joyful or moved. The idea suggestion unit also uses the generation AI to estimate the recipient's emotions and suggest a gift based on those emotions. For example, it suggests items that will help the recipient relax or gifts that will help relieve stress. The idea suggestion unit also uses the emotion estimation function to estimate the recipient's emotions and suggest a gift that best suits those emotions. For example, it selects a gift that will make the recipient feel gratitude or love. This makes it possible to suggest the optimal gift based on the recipient's emotions.

[0056] The personalized gift suggestion unit analyzes the recipient's past purchase history and reviews to suggest more accurate personalized gifts. For example, the personalized gift suggestion unit uses a generation AI to analyze the recipient's past purchase history and reviews to suggest more accurate personalized gifts. For example, it suggests gifts similar to products that the recipient has given high ratings to in the past. The personalized gift suggestion unit also uses a generation AI to suggest personalized gifts based on the recipient's past purchase history and reviews. For example, it suggests products from brands and categories that the recipient frequently purchases. The personalized gift suggestion unit also uses a generation AI to analyze the recipient's purchase history and reviews to suggest personalized gifts. For example, it selects the most appropriate gift based on the reviews of products the recipient has purchased in the past. This enables more accurate suggestions to be made based on the recipient's past purchase history and reviews.

[0057] The personalized gift suggestion unit can collect the opinions of the recipient's family and friends and suggest the optimal gift from multiple perspectives. In the personalized gift suggestion unit, for example, the generation AI collects the opinions of the recipient's family and friends and suggests the optimal gift from multiple perspectives. For example, it suggests gifts that family and friends would like to give to the recipient. In addition, the personalized gift suggestion unit uses the generation AI to suggest the optimal gift based on the opinions of the recipient's family and friends. For example, if family and friends know the recipient's hobbies and preferences, it uses that information to make suggestions. In addition, the personalized gift suggestion unit uses the generation AI to collect the opinions of family and friends and suggest the optimal gift from multiple perspectives. For example, it collects gifts that family and friends would like to give to the recipient in the form of a questionnaire and makes suggestions based on the results. This makes it possible to make optimal suggestions from multiple perspectives based on the opinions of the recipient's family and friends.

[0058] The personalized gift suggestion unit can use the emotion estimation function to estimate the recipient's emotions and suggest a customized gift based on those emotions. The personalized gift suggestion unit, for example, uses the emotion estimation function to estimate the recipient's emotions and suggest a customized gift based on those emotions. For example, it selects a gift that will make the recipient feel joyful or moved. The personalized gift suggestion unit also uses the generation AI to estimate the recipient's emotions and suggest a customized gift based on those emotions. For example, it suggests items that will help the recipient relax or gifts that will help relieve stress. The personalized gift suggestion unit also uses the emotion estimation function to estimate the recipient's emotions and suggest a customized gift based on those emotions. For example, it selects a gift that will make the recipient feel gratitude or love. This makes it possible to suggest customized gifts based on the recipient's emotions.

[0059] The cost management unit can analyze a user's past spending history and suggest the optimal gift within a budget. In the cost management unit, for example, the generation AI analyzes a user's past spending history and suggests the optimal gift within a budget. For example, suggestions are made based on data on the amount of money the user has spent in the past and the products they have purchased. In addition, the cost management unit can use the generation AI to suggest the optimal gift within a budget based on the user's past spending history. For example, suggestions are made taking into account the price range and frequency of gifts the user has purchased in the past. In addition, the cost management unit can use the generation AI to analyze a user's spending history and suggest the optimal gift within a budget. For example, the generation AI can select the optimal gift within a budget based on ratings and reviews of gifts the user has purchased in the past. This makes it possible to make optimal suggestions within a budget based on the user's past spending history.

[0060] The cost management department can monitor market price fluctuations in real time and suggest the optimal timing for purchases. For example, the generation AI in the cost management department monitors market price fluctuations in real time and suggests the optimal timing for purchases. For example, it predicts when a particular product will go on sale and makes a suggestion. The cost management department also uses the generation AI to suggest the optimal timing for purchases based on market price fluctuations. For example, it predicts when prices will fall and suggests purchasing at that time. The cost management department also uses the generation AI to monitor market price fluctuations in real time and suggests the optimal timing for purchases. For example, it suggests purchasing during a particular event or sale period. This makes it possible to suggest the optimal timing for purchases based on market price fluctuations.

[0061] The cost management unit can use the emotion estimation function to suggest gifts that will give the user satisfaction within the user's budget. For example, the cost management unit uses the emotion estimation function to suggest gifts that will give the user satisfaction within the user's budget. For example, it selects gifts that will make the user feel joyful or moved. The cost management unit also uses the generation AI to estimate the user's emotions and suggest gifts that will give the user satisfaction within the user's budget. For example, it suggests items that will help the user relax or gifts that will help relieve stress. The cost management unit also uses the emotion estimation function to suggest gifts that will give the user satisfaction within the user's budget. For example, it selects gifts that will make the user feel grateful or loved. This makes it possible to suggest gifts that will give the user satisfaction within the user's budget based on the user's emotions.

[0062] The schedule management unit can analyze the user's calendar and schedule and suggest the optimal timing for gift-giving. In the schedule management unit, for example, the generation AI analyzes the user's calendar and schedule and suggests the optimal timing for gift-giving. For example, it suggests a timing for gift-giving that matches the user's plans. In addition, the schedule management unit can have the generation AI analyze the user's calendar and schedule and suggest the optimal timing for gift-giving. For example, it can make suggestions to match specific events or anniversaries. In addition, the schedule management unit can have the generation AI analyze the user's calendar and schedule and suggest the optimal timing for gift-giving. For example, it can suggest a timing for gift-giving that avoids the user's busy periods. This makes it possible to suggest the optimal timing for gift-giving based on the user's calendar and schedule.

[0063] The schedule management unit can learn past gift-giving history and predict the timing of the next gift-giving. In the schedule management unit, for example, the generation AI learns past gift-giving history and predicts the timing of the next gift-giving. For example, it makes suggestions based on the timing of gifts given by the user in the past. In addition, the schedule management unit predicts the timing of the next gift-giving based on the past gift-giving history. For example, it makes suggestions to coincide with specific events or anniversaries. In addition, the schedule management unit predicts the timing of the next gift-giving based on the past gift-giving history. For example, it makes suggestions taking into account the frequency and timing of gifts given by the user in the past. This makes it possible to predict the timing of the next gift-giving based on the past gift-giving history.

[0064] The schedule management unit can use the emotion estimation function to emotionally emphasize and remind the user of important dates that the user tends to forget. For example, the schedule management unit uses the emotion estimation function to emotionally emphasize and remind the user of important dates that the user tends to forget. For example, it sends a message that will move the user. Furthermore, the schedule management unit uses the generation AI to estimate the user's emotions and emotionally emphasize and remind the user of important dates. For example, it sends a reminder that makes the user feel joy or emotion. Furthermore, the schedule management unit uses the emotion estimation function to emotionally emphasize and remind the user of important dates that the user tends to forget. For example, it sends a message that makes the user feel gratitude or love. In this way, by emotionally emphasizing and reminding the user of important dates that the user tends to forget, the user can avoid missing important dates.

[0065] The burden reduction unit can analyze the user's past gift selection process and propose the most efficient selection method. In the burden reduction unit, for example, the generation AI analyzes the user's past gift selection process and proposes the most efficient selection method. For example, the proposal is made based on the selection process of gifts selected by the user in the past. In addition, the burden reduction unit proposes the most efficient selection method based on the user's past gift selection process. For example, the proposal is made taking into account the selection time and steps of gifts selected by the user in the past. In addition, the burden reduction unit proposes the most efficient selection method based on the user's past gift selection process. For example, the proposal is made based on the ratings and reviews of gifts selected by the user in the past. This makes it possible to propose an efficient selection method based on the user's past selection process.

[0066] The burden reduction unit can monitor the user's stress level and make relaxation suggestions when stress increases. For example, the generation AI in the burden reduction unit monitors the user's stress level and makes relaxation suggestions when stress increases. For example, if the user is feeling stressed, it suggests relaxation music or a meditation app. The generation AI in the burden reduction unit also makes relaxation suggestions based on the user's stress level. For example, if the user is feeling stressed, it suggests relaxation goods or aroma oils. The generation AI in the burden reduction unit also monitors the user's stress level and makes relaxation suggestions when stress increases. For example, it makes suggestions to provide an environment where the user can relax. This makes it possible to make relaxation suggestions based on the user's stress level.

[0067] The burden reduction unit can use the emotion estimation function to provide a gift selection environment in which the user can be most relaxed. For example, the burden reduction unit uses the emotion estimation function to provide a gift selection environment in which the user can be most relaxed. For example, it provides music or videos that the user can use to relax. The burden reduction unit also uses the generation AI to estimate the user's emotions and provides a gift selection environment in which the user can be most relaxed. For example, it provides an interface or design that the user can use to relax. The burden reduction unit also uses the emotion estimation function to provide a gift selection environment in which the user can be most relaxed. For example, it provides aromas or lighting that the user can use to relax. This makes it possible to provide a gift selection environment in which the user can be most relaxed.

[0068] The Regional Industry Revitalization Department can work directly with producers of regional specialty products and provide the latest information. For example, the generation AI in the Regional Industry Revitalization Department works directly with producers of regional specialty products and provides the latest information. For example, it provides users with the latest arrival information from producers and the characteristics of specialty products. The Regional Industry Revitalization Department also works with producers of regional specialty products and the generation AI provides the latest information. For example, it provides users with information from producers about recommended products and seasonal limited items. The Regional Industry Revitalization Department also works directly with producers of regional specialty products and provides the latest information. For example, it provides users with information from producers about the manufacturing process and background of specialty products. In this way, by working directly with producers of regional specialty products and providing the latest information, it is possible to revitalize local industries.

[0069] The Regional Industry Revitalization Department can analyze local event information and suggest related specialty products. For example, the generation AI in the Regional Industry Revitalization Department analyzes local event information and suggests related specialty products. For example, it suggests specialty products that match local festivals and events. The Regional Industry Revitalization Department also uses the generation AI to suggest related specialty products based on local event information. For example, it suggests specialty products that match specific events or seasons. The Regional Industry Revitalization Department also uses the generation AI to analyze local event information and suggest related specialty products. For example, it suggests specialty products that are popular at local events or limited edition products. This makes it possible to suggest specialty products based on local event information.

[0070] The Regional Industry Revitalization Department can use the emotion estimation function to make optimal suggestions based on the emotions a user feels toward regional specialty products. For example, the Regional Industry Revitalization Department uses the emotion estimation function to make optimal suggestions based on the emotions a user feels toward regional specialty products. For example, it can suggest specialty products that make the user feel joy or emotion. The Regional Industry Revitalization Department also uses the generation AI to estimate the user's emotions and make optimal suggestions based on the emotions the user feels toward regional specialty products. For example, it can suggest specialty products that help the user relax or relieve stress. The Regional Industry Revitalization Department also uses the emotion estimation function to make optimal suggestions based on the emotions the user feels toward regional specialty products. For example, it can suggest specialty products that make the user feel gratitude or affection. This makes it possible to make optimal suggestions for regional specialty products based on the user's emotions.

[0071] The Regional Industry Revitalization Department can attract users' interest by providing stories and background information about regional specialty products. For example, the generation AI can provide stories and background information about regional specialty products to attract users' interest. For example, it can introduce the manufacturing process of the specialty product or anecdotes about the producer. The Regional Industry Revitalization Department also uses the generation AI to make suggestions that will attract users' interest based on the stories and background information about the regional specialty product. For example, it can introduce the history and traditions of the specialty product. The Regional Industry Revitalization Department can also use the generation AI to provide stories and background information about regional specialty products to attract users' interest. For example, it can introduce the manufacturing method of the specialty product and the characteristics of the materials used. In this way, it is possible to attract users' interest by providing stories and background information about the regional specialty product.

[0072] The Regional Industry Revitalization Department can propose tasting events and experience tours for regional specialty products. For example, the generation AI in the Regional Industry Revitalization Department proposes tasting events and experience tours for regional specialty products. For example, it proposes tasting events for specialty products and events to interact with producers. The Regional Industry Revitalization Department also makes suggestions to users based on tasting events and experience tours for regional specialty products. For example, it proposes tours that allow visitors to see the manufacturing site of specialty products. The generation AI in the Regional Industry Revitalization Department also proposes tasting events and experience tours for regional specialty products. For example, it proposes harvesting experiences for specialty products and cooking classes. This makes it possible to propose tasting events and experience tours for regional specialty products.

[0073] The regional industry revitalization department can use the emotion estimation function to make suggestions that will increase the emotional satisfaction a user feels when purchasing regional specialty products. For example, the regional industry revitalization department uses the emotion estimation function to make suggestions that will increase the emotional satisfaction a user feels when purchasing regional specialty products. For example, it suggests specialty products that make the user feel joy or emotion. Furthermore, the regional industry revitalization department uses the generation AI to estimate the user's emotions and makes suggestions that will increase the emotional satisfaction a user feels when purchasing regional specialty products. For example, it suggests specialty products that help the user relax or relieve stress. Furthermore, the regional industry revitalization department uses the emotion estimation function to make suggestions that will increase the emotional satisfaction a user feels when purchasing regional specialty products. For example, it suggests specialty products that make the user feel gratitude or affection. This makes it possible to make suggestions that will increase satisfaction when purchasing regional specialty products based on the user's emotions.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The gift communication system may further include an environmental consideration unit. The environmental consideration unit makes suggestions for minimizing the impact on the environment when selecting a gift. For example, the environmental consideration unit may suggest gifts made from eco-friendly materials or reusable packaging. The environmental consideration unit may also prioritize locally produced products or gifts with short delivery distances in order to reduce the carbon footprint. Furthermore, the environmental consideration unit may provide the user with a message encouraging environmental consideration when selecting a gift. This allows the user to make environmentally friendly choices and contribute to the realization of a sustainable society.

[0076] The gift communication system may further include a health management unit. The health management unit suggests health-conscious gifts based on the user's health condition and lifestyle. For example, the health management unit may suggest appropriate food gifts taking into account the user's dietary restrictions and allergy information. The health management unit may also suggest sports equipment and fitness-related gifts based on the user's exercise habits and fitness goals. Furthermore, the health management unit may monitor the user's stress level and sleep patterns and suggest gifts that are useful for relaxation and stress relief. This allows the user to select appropriate gifts while maintaining their health.

[0077] The gift communication system may further include an education support unit. The education support unit suggests education-related gifts based on the user's learning needs and interests. For example, the education support unit may suggest books and online courses related to the field the user wants to study. The education support unit may also suggest appropriate educational toys and teaching materials based on the user's child's learning progress and interests. Furthermore, the education support unit may provide information on workshops and seminars that the user can participate in to expand learning opportunities. This allows the user to select gifts that meet their own and their family's learning needs.

[0078] The gift communication system may further include a cultural exchange section. The cultural exchange section may suggest gifts that promote intercultural understanding and international exchange. For example, the cultural exchange section may suggest traditional crafts or specialty products from countries or regions in which the user is interested. The cultural exchange section may also provide information on events and tours that allow the user to experience different cultures. Furthermore, the cultural exchange section may suggest books or documentaries that will help the user deepen their knowledge of different cultures. This allows the user to enjoy international exchange through gifts while deepening their understanding of different cultures.

[0079] The gift communication system can further use an emotion estimation function to estimate the user's emotions and provide advice on gift selection based on those emotions. For example, if the emotion estimation function is used to estimate the user's emotions, it can suggest gifts that will help them relax and relieve stress. Also, if the emotion estimation function is used to estimate the user's emotions, it can suggest gifts that will further enhance those emotions. Furthermore, if the user is feeling gratitude or love, it can suggest gifts that will express those emotions. This makes it possible to select the optimal gift based on the user's emotions.

[0080] The gift communication system can also use an emotion estimation function to estimate the recipient's emotions and customize the gift based on those emotions. For example, if the emotion estimation function is used to indicate that the recipient is feeling joy or emotion, a message or design that will further enhance those emotions can be added. Also, if the emotion estimation function is used to indicate that the recipient is seeking relaxation, the system can customize and suggest relaxation items or stress relief goods. Furthermore, if the emotion estimation function is used to indicate that the recipient is feeling gratitude or love, the system can suggest a special message or personalized gift to express those emotions. This makes it possible to optimally customize gifts based on the recipient's emotions.

[0081] The gift communication system can further use an emotion estimation function to estimate the user's emotions and optimize the gift selection process based on those emotions. For example, if the emotion estimation function is used to estimate the user's emotions, the system can simplify the selection process and provide support to reduce stress. Also, if the emotion estimation function is used to estimate the user's emotions, the system can provide a fun selection process to maintain those emotions. Furthermore, if the emotion estimation function is used to estimate the user's emotions, the system can provide a selection process that reflects those emotions. This enables an optimal gift selection process based on the user's emotions.

[0082] The gift communication system can further use an emotion estimation function to estimate the recipient's emotions and optimize the timing of gift delivery based on those emotions. For example, if the emotion estimation function is used to determine that the recipient is feeling joy or emotion, the gift can be delivered at a specific time to maximize that emotion. Also, if the emotion estimation function is used to determine that the recipient is seeking relaxation, the gift can be delivered at a time when the recipient is feeling relaxed. Furthermore, if the emotion estimation function is used to determine that the recipient is feeling gratitude or love, the gift can be delivered at a special time to express that emotion. This makes it possible to optimally time gift delivery based on the recipient's emotions.

[0083] The gift communication system can further use an emotion estimation function to estimate the user's emotions and customize gift packaging based on those emotions. For example, if the emotion estimation function indicates that the user is feeling joy or emotion, it can add a design or message to the package that further enhances those emotions. Also, if the emotion estimation function indicates that the user is seeking relaxation, it can suggest packaging using colors and materials that are relaxing. Furthermore, if the emotion estimation function indicates that the user is feeling gratitude or love, it can add a special message or design to the package to express those emotions. This makes it possible to create optimal gift packaging based on the user's emotions.

[0084] The gift communication system can also analyze a user's past gift selection history without using emotion estimation functions, learn selection trends, and reflect them in future suggestions. For example, the types and price ranges of gifts given in the past, as well as the recipients' reactions, can be saved in a database and reflected in future suggestions. Selection trends can also be learned based on the user's past gift selection history and used in future suggestions. For example, if there is a bias toward a particular brand or category, suggestions can be made taking these trends into account. Furthermore, more appropriate suggestions can be made based on ratings and reviews of gifts given in the past. This enables more accurate suggestions based on the user's past selection history.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The idea suggestion unit suggests optimal gift ideas based on the latest trends and past history, taking into account the recipient's type, preferences, and occasion. For example, if a user asks for advice such as "I don't know what to give for Mother's Day," the AI ​​generator will suggest "gifts that match Mom's favorite flowers and hobbies." Step 2: The personalized gift suggestion department proposes customized and personalized gifts tailored to the recipient's personality. For example, it can analyze the recipient's past purchase history and preferences to select the most suitable gift, and can also suggest travel, activity experiences, meal vouchers, etc. Step 3: The cost management department suggests the best gift within the user's budget. For example, it receives a prompt such as "I'm looking for a gift that matches my hobbies and has a budget of 5,000 yen or less," and suggests the best gift within that range. Step 4: The schedule management section will notify you of gift-giving timings that you may forget. For example, it will send a reminder such as, "It's Mom's birthday next week. Have you prepared a gift?" Step 5: The burden reduction department supports users from gift ideas to delivery. For example, by "handling everything from gift selection to purchase and delivery arrangements all in one place," the department reduces the stress and burden on users. Step 6: The Regional Industry Revitalization Department will provide information on regional specialties and local industries. For example, they will propose "roadside station products" and "hometown tax donation products" to revitalize regional industries.

[0087] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0092] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0093] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0094] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0097] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0098] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0100] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0101] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0128] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0137] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0138] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0139] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0140] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0141] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0142] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0143] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0144] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0145] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0146] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0147] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0148] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0149] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0150] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0151] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0152] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0153] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0154] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Idea Suggestion Department proposes the best gift ideas based on the latest trends and past history, according to the recipient's type, preferences, and occasion. The personalized gift proposal department proposes customized and personalized gifts that fit the recipient's personality, The cost management department proposes the best gifts for users within their budget, A schedule management department that notifies you of gift-giving timing that you may forget, We have a burden reduction department that supports you from gift ideas to delivery, The Regional Industry Revitalization Department provides information on local specialties and local industries. A system characterized by:

2. The idea proposal unit Analyze the user's past gift selection history, learn their selection trends, and reflect them in the next recommendation.

2. The system of claim 1.

3. The idea proposal unit Analyze the recipient's public information on social media, blogs, etc., and suggest gifts that reflect their interests.

2. The system of claim 1.

4. The idea proposal unit Predict the recipient's emotions and suggest a gift that best suits those emotions 2. The system of claim 1.

5. The personalized gift suggestion unit Analyze the recipient's past purchase history and reviews to suggest more personalized gifts 2. The system of claim 1.

6. The personalized gift suggestion unit Collect opinions from the recipient's family and friends and propose the best gift from multiple perspectives 2. The system of claim 1.

Images